Asynchronous data replication between two types of databases involves processing source data changes from a source system and effectively applying (i.e., replicating) the source data changes to a target system. Typically asynchronous data replication involves committing source data changes in the source system and then replicating the source data changes to a target system. Subsequently, the data is replicated to target systems for scheduled intervals. For example, a financial institution may use a source system daily (e.g., an OLTP system) to rapidly execute financial information and update financial records. In this instance, the financial institution can asynchronously replicate data to a target system (e.g., an OLAP system) at the end of each month to aggregate and consolidate financial information and records.
A typical process for asynchronous data replication can be useful to replicate source data from an OLTP system to an OLAP system. The process may also require large computational overhead and a long apply time to replicate source data changes. For example, applying a single source data change can involve multiple statements to be applied to a single target table of a target system. Extrapolating this instance to many source data changes from multiple source systems to be applied to more than one target system can lead to a cost-ineffective and a lengthy duration of time for data replication.